66,812 research outputs found
Automatic generation of alignments for 3D QSAR analyses
Many 3D QSAR methods require the alignment of the molecules in a dataset, which can require a fair amount of manual effort in deciding upon a rational basis for the superposition. This paper describes the use of FBSS, a pro-ram for field-based similarity searching in chemical databases, for generating such alignments automatically. The CoMFA and CoMSIA experiments with several literature datasets show that the QSAR models resulting from the FBSS alignments are broadly comparable in predictive performance with the models resulting from manual alignments
Age Progression and Regression with Spatial Attention Modules
Age progression and regression refers to aesthetically render-ing a given
face image to present effects of face aging and rejuvenation, respectively.
Although numerous studies have been conducted in this topic, there are two
major problems: 1) multiple models are usually trained to simulate different
age mappings, and 2) the photo-realism of generated face images is heavily
influenced by the variation of training images in terms of pose, illumination,
and background. To address these issues, in this paper, we propose a framework
based on conditional Generative Adversarial Networks (cGANs) to achieve age
progression and regression simultaneously. Particularly, since face aging and
rejuvenation are largely different in terms of image translation patterns, we
model these two processes using two separate generators, each dedicated to one
age changing process. In addition, we exploit spatial attention mechanisms to
limit image modifications to regions closely related to age changes, so that
images with high visual fidelity could be synthesized for in-the-wild cases.
Experiments on multiple datasets demonstrate the ability of our model in
synthesizing lifelike face images at desired ages with personalized features
well preserved, and keeping age-irrelevant regions unchanged
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